A novel classification method based on SVM is proposed for binary classification tasks of homogeneous data in this paper. The proposed method can effectively predict the binary labeling of the sequence of observation samples in the test set by using the following procedure: we first make different assumptions about the class labeling of this sequence, then we utilize SVM to obtain two classification errors respectively for each assumption, and finally the binary labeling is determined by comparing the obtained two classification errors. The proposed method leverages the homogeneity within the same classes and exploits the difference between different classes, and hence can achieve the effective classification for homogeneous data. Experimental results indicate the power of the proposed method.
|Number of pages||8|
|Journal||Applied Soft Computing Journal|
|Publication status||Published - 11 Aug 2015|
- Homogeneous data
- Multi-observation samples
- SVM classification
ASJC Scopus subject areas